A parallel algorithm for statistical belief refinement and its use in causal reasoning

Authors:
Jay C. Weber
Affiliations:
Computer Science Department, University of Rochester, Rochester, NY
Venue:
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Year:
1989

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Abstract

This paper presents a new approach to efficient parallel computation of statistical inferences. This approach involves two heuristics, highest impact first and highest impact remaining, which control the speed of convergence and error estimation for an algorithm that iteratively refines degrees of belief. When applied to causal reasoning, this algorithm provides a performance solution to the qualification problem. This algorithm has been implemented and tested by a program called HITEST, which runs on parallel hardware.